Résultats de recherche pour « Temporal Fusion Transformer »

Infrared and visible image fusion (IVIF) is essential for integrating thermal saliency with textural details to support downstream perception. However, most existing approaches suffer from "semantic blindness," leading to the erroneous suppression of thermal targets and the introduction of visual artifacts. To address this, we propose SAM-Guided Diffusion Fusion Network (SGDFuse), a novel Semantic-Guided Generation (SGG) framework that reframes IVIF as a semantically-steered generative task rather than simplistic pixel mapping. Our method uniquely couples high-level semantic priors from the Segment Anything Model (SAM) with the high-fidelity generative power of a conditional diffusion model. We employ a deliberate two-stage strategy to decouple multimodal alignment from iterative refinement: Stage I establishes a robust structural foundation via preliminary fusion, while Stage II utilizes dual-modality semantic masks as spatial anchors to guide the diffusion process toward a semantically coherent, high-fidelity reconstruction. Comprehensive experiments demonstrate that SGDFuse not only delivers state-of-the-art image quality but also enhances downstream task performance, confirming its effectiveness as a new Methodological Framework for semantically aware image fusion. The code is available at https://github.com/boshizhang123/SGDFuse.

Bu çalışmada, derin öğrenme modellerinden biri olan Temporal Fusion Transformer (TFT) mimarisi kullanılarak Türkiye’ye ait aylık İnşaat Maliyet Endeksi (İME) öngörüsü gerçekleştirilmeye çalışılmıştır. Burada, günümüzde öngörüde kullanılan derin öğrenme modellerinden biri olan TFT modelini tanıtmak ve inşaat maliyet endeksi öngörüsünde kullanarak performansını görmek amaçlanmıştır. İME öngörüsü için, Ocak 2015-Eylül 2025 zaman aralığında aylık veriler kullanılmıştır. Ayrıca, maliyet dinamiklerini etkileyebileceği değerlendirilen üç bağımsız değişken (inşaat yapı sayısı (), sanayi üretim endeksi, konut satış endeksi) seçilerek modele dâhil edilmiştir. Öngörüde TFT modeli kullanılırken, model performansı Kök Ortalama Kare Hata (RMSE) metriği ile değerlendirilmiştir. TFT modelinin RMSE değeri 36,01 olarak bulunmuştur. Elde edilen bulgular, TFT modelinin hem kısa vadeli öngörülerde hem de tüm dönem performansında yüksek doğruluk sağladığını ve trend ile volatilite dinamiklerini başarılı biçimde yakaladığını göstermektedir. Ayrıca değişken önem analizi sayesinde, inşaat maliyetlerini etkileyen temel göstergelerin göreli katkıları nicel olarak ortaya konulmuştur.

There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. However, using AI comes with its own challenges, many of which can be mitigated if responsible and robust methodologies are built into existing approaches. To do that requires close, long-term collaborations between fusion domain experts and AI developers and awareness of the fact that not all problems in fusion research are best tackled with AI tools. In April 2025, experts from academia, industry, UKAEA and STFC discussed how AI can be used to advance R&D in fusion energy at the first edition of The Economist FusionFest event. This Perspective is an expanded and updated summary of the round table discussion, providing more context and examples.

Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, and retrieval-augmented generation (RAG) into a single mathematical architecture. The predictive backbone employs a graph-augmented Temporal Fusion Transformer to capture non-linear temporal dependencies and spatial disease propagation. By formalizing regional topology and mobility flows as a weighted mathematical graph, the model systematically integrates structured epidemiological counts, continuous environmental covariates, and digital trace signals. To address algorithmic bias, we formulate a fairness-aware optimization problem by embedding a specific regularization term into the training objective, which mathematically penalizes disparities in true-positive rates across diverse socio-demographic strata. Furthermore, the numerical outputs and anomaly scores are processed by a large language model equipped with hybrid dense and sparse retrieval to generate interpretable, computationally grounded decision support. Extensive experiments on a longitudinal dataset comprising 62 administrative regions over 260 weeks validate the mathematical robustness of the proposed framework. The graph-augmented architecture improved forecasting accuracy by up to 24% and anomaly detection F1 scores by over 6% compared to state-of-the-art deep learning baselines, while the fairness-regularized loss function reduced the maximum subgroup recall gap by more than 50%. These findings demonstrate that predictive accuracy and algorithmic fairness can be jointly optimized, providing a rigorous computational methodology for spatio-temporal epidemic modeling and AI-driven surveillance.

Recognizing facial activity is a well-understood (but non-trivial) computer vision problem. However, reliable solutions require a camera with a good view of the face, which is often unavailable in wearable settings. Furthermore, in wearable applications, where systems accompany users throughout their daily activities, a permanently running camera can be problematic for privacy (and legal) reasons. This work presents an alternative solution based on the fusion of wearable inertial sensors, planar pressure sensors, and acoustic mechanomyography (muscle sounds). The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions. We present our integrated wearable sensor system, describe data fusion and analysis methods, and evaluate the system in an experiment with thirteen subjects from different cultural backgrounds (eight countries) and both sexes (six women and seven men). In a one-model-per-user scheme and using a late fusion approach, the system yielded an average F1 score of 85.00% for the case where all sensing modalities are combined. With a cross-user validation and a one-model-for-all-user scheme, an F1 score of 79.00% was obtained for thirteen participants (six females and seven males). Moreover, in a hybrid fusion (cross-user) approach and six classes, an average F1 score of 82.00% was obtained for eight users. The results are competitive with state-of-the-art non-camera-based solutions for a cross-user study. In addition, our unique set of participants demonstrates the inclusiveness and generalizability of the approach.

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial Immune Systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has show how AISs which use multi-level information sources as input data can be used to build effective algorithms for real time computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs

Accurately forecasting lithium carbonate prices is essential for strategic planning in the global energy-storage and electric-vehicle industries. This study proposes a Temporal Fusion Transformer (TFT)–based forecasting framework to model daily lithium market dynamics using multi-source historical data from January 2012 to September 2025. The model leverages attention mechanisms, temporal gating, and variable-importance learning. Using these learned dynamics, the TFT model generates long-term forecasts for 2026–2030, producing smooth, stable, and economically plausible price trajectories. Its performance is compared with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) baselines, where absolute-error box plots and numerical performance metrics demonstrate that the TFT model yields substantially lower variance, reduced forecast deviation, and more realistic long-horizon behavior. From the perspective of explainable artificial intelligence (XAI), the research findings provide a diverse range of stakeholders in lithium-dependent industries with reliable, transparent, and interpretable insights, thereby effectively supporting their long-term strategic decision-making processes regarding critical raw material price forecasts.

This paper provides a retrospective of the ALPHA (Accelerating Low-cost Plasma Heating and Assembly) fusion program of the Advanced Research Projects Agency-Energy (ARPA-E) of the U.S. Department of Energy. ALPHA's objective was to catalyze research and development efforts to enable substantially lower-cost pathways to economical fusion power. To do this in a targeted, focused program, ALPHA focused on advancing the science and technology of pulsed, intermediate-density fusion approaches, including magneto-inertial fusion and Z-pinch variants, that have the potential to scale to commercially viable fusion power plants. The paper includes a discussion of the origins and framing of the ALPHA program, a summary of project status and outcomes, a description of associated technology-transition activities, and thoughts on a potential follow-on ARPA-E fusion program.

Abstract Urban traffic flow prediction is critical for intelligent transportation systems, yet remains challenging due to complex spatiotemporal dependencies and external weather influences. Existing methods suffer from three key limitations: (1) uniform time-domain modeling fails to disentangle periodic trends from aperiodic fluctuations, (2) weather information is integrated through simple concatenation without considering multi-granularity impacts, and (3) lack of interpretability hinders understanding of weather-traffic interactions. To address these issues, we propose MFTFormer, a novel framework that synergistically combines time-frequency decomposition with hierarchical weather-aware fusion. Our approach introduces three key innovations: (1) a dual-domain encoder with mixture-of-experts that separately processes trend and seasonal components via specialized frequency and temporal attention networks, (2) a block-aligned fusion module that hierarchically reorganizes features through convolutional refinement and weather-conditioned modulation, capturing both immediate and cumulative meteorological effects, and (3) interpretable attention mechanisms that reveal dynamic spatial dependencies under varying weather conditions. Extensive experiments on four real-world datasets (PeMS04, PeMS08, Metro-Traffic, METR-LA) demonstrate that MFTFormer achieves state-of-the-art performance with an average 6.2% MAE reduction over strong baselines, while providing actionable insights into weather-traffic relationships through attention visualization.